Fetching the paper…
Reading the bibliography…
Artificial Intelligence has the potential to exacerbate societal bias and set back decades of advances in equal rights and civil liberty.
The devil and the one drop rule: Racial categories, african americans, and the us census
Christine B Hickman · 1997
Earlier work this paper cites.
Minimising decision tree size as combinatorial optimisation
Christian Bessiere, Emmanuel Hebrard, and Barry O’Sullivan · 2009
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Faisal Kamiran and Toon Calders · 2012
Earlier work this paper cites.
Automated experiments on ad privacy settings
Amit Datta, Michael Carl Tschantz, and Anupam Datta · 2015
Earlier work this paper cites.
Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
Earlier work this paper cites.
Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai · 2016
Earlier work this paper cites.
To predict and serve?
Kristian Lum and William Isaac · 2016
Earlier work this paper cites.
Weapons of math destruction: How big data increases inequality and threatens democracy
Cathy O’Neil · 2016
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan · 2017
Earlier work this paper cites.
Iso 26000 social responsibiliy, 2017
ISO · 2017
Earlier work this paper cites.
Inherent trade-offs in the fair determination of risk scores
Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Earlier work this paper cites.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
Earlier work this paper cites.
Anatomy of an ai system
Kate Crawford and Vladan Joler · 2018
Cited alongside, same era.
Automating inequality: How high-tech tools profile, police, and punish the poor
Virginia Eubanks · 2018
Cited alongside, same era.
Beijing’s big brother tech needs african faces
Amy Hawkins · 2018
Cited alongside, same era.
Artificial unintelligence: How computers misunderstand the world, 2018
B Meredith · 2018
Cited alongside, same era.
Algorithms of oppression: How search engines reinforce racism
Safiya Umoja Noble · 2018
Cited alongside, same era.
Reducing gender bias in abusive language detection
Ji Ho Park, Jamin Shin, and Pascale Fung · 2018
Cited alongside, same era.
Oxford handbook on ai ethics book chapter on race and gender
Timnit Gebru · 2019
Later among the works it cites.
Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads
Anja Lambrecht and Catherine Tucker · 2019
Later among the works it cites.
A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2019
Later among the works it cites.
Dissecting racial bias in an algorithm used to manage the health of populations
Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan · 2019
Later among the works it cites.
Oecd council recommendation on artificial intelligence, 2019
OECD · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Outnumbered: From Facebook and Google to Fake News and Filter-bubbles – The Algorithms That Control Our Lives
David Sumpter · 2018
Cited alongside, same era.
Gender bias in coreference resolution: Evaluation and debiasing methods
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, and Kai-Wei Chang · 2018
Cited alongside, same era.
Designing a value-driven future for ethical autonomous and intelligent systems
G. Adamson, J. C. Havens, and R. Chatila · 2019
Cited alongside, same era.
Race after technology: Abolitionist tools for the new jim code
Ruha Benjamin · 2019
Cited alongside, same era.
Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way
Virginia Dignum · 2019
Cited alongside, same era.
High-level expert group on artificial intelligence: Ethics guidelines for trustworthy ai
EU-HLEG-AI · 2019
Cited alongside, same era.
Invisible Women: Exposing data bias in a world designed for men
Caroline Criado Perez · 2019
Later among the works it cites.
Dirty data, bad predictions: How civil rights violations impact police data, predictive policing systems, and justice
Rashida Richardson, Jason Schultz, and Kate Crawford · 2019
Later among the works it cites.
Assessing social and intersectional biases in contextualized word representations
Yi Chern Tan and L Elisa Celis · 2019
Later among the works it cites.
Guidance for regulation of artificial intelligence applications, 2019
US-Govt · 2019
Later among the works it cites.
A right to reasonable inferences: re-thinking data protection law in the age of big data and ai
Sandra Wachter and Brent Mittelstadt · 2019
Later among the works it cites.
An Artificial Revolution: On Power, Politics and AI
Ivana Bartoletti · 2020
Closest in time.
Data feminism
Catherine D’Ignazio and Lauren F Klein · 2020
Closest in time.